SafePathAI: Uncertainty-Aware Trajectory Prediction

Overview

SafePathAI is an advanced trajectory prediction framework designed for safety-critical autonomous systems, including autonomous vehicles, drones, and maritime navigation. The project focuses on developing robust trajectory prediction models with comprehensive uncertainty estimation techniques.

Key Features

• Hybrid Prediction Model: Combines Kalman Filter with Deep Learning LSTM networks
• Advanced Uncertainty Estimation:
  • Aleatoric Uncertainty
  • Epistemic Uncertainty
  • Ensemble-based Uncertainty Quantification
• Rejection Mechanism: Ability to abstain from predictions with high uncertainty
• Multi-Domain Support: Tested across autonomous driving, drone navigation, and maritime tracking datasets

Installation

Prerequisites

• Python 3.8+
• CUDA-compatible GPU (recommended)
• PyTorch
• NumPy
• Matplotlib
• scikit-learn

Setup

# Clone the repository
git clone https://github.com/yourusername/SafePathAI.git
cd SafePathAI

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Usage

Training the Model

from safepath_ai.main import main

# Train the hybrid trajectory prediction model
main()

Configuration

Modify Config class parameters in the source code to adjust:

• Input/prediction sequence lengths
• Model hyperparameters
• Training settings
• Uncertainty thresholds

Supported Datasets

• nuScenes
• Argoverse Motion Forecasting
• DUT Dataset
• MarineTraffic Dataset
• CrowdFlow Dataset

Methodology

1. Kalman Filter: Provides classical state estimation
2. LSTM with Attention: Captures complex movement patterns
3. Ensemble Learning: Improves prediction reliability
4. Uncertainty Quantification:
   • Aleatoric Uncertainty: Model's inherent noise
   • Epistemic Uncertainty: Model's knowledge uncertainty

Visualization

The framework includes visualization tools to help understand:

• Trajectory predictions
• Prediction uncertainties
• Confidence intervals

Research Objectives

• Improve trajectory prediction reliability
• Quantify and manage prediction uncertainties
• Enable risk-aware decision-making in autonomous systems

Limitations

• Computational complexity of uncertainty estimation
• Performance variations across different domains
• Sensitivity to input data quality

Future Work

• Improve real-time performance
• Extend to more complex scenarios
• Develop more advanced uncertainty quantification techniques

Contributing

Contributions are welcome! Please read the contributing guidelines before submitting pull requests.

License

[Specify your license here]

Citation

If you use SafePathAI in your research, please cite our work:

@misc{SafePathAI2025,
  title={SafePathAI: Uncertainty-Aware Trajectory Prediction},
  author={Your Name},
  year={2025}
}

Contact

[Your Contact Information]